Emulating galaxy clustering and galaxy-galaxy lensing into the deeply nonlinear regime: methodology, information, and forecasts
Benjamin D. Wibking, Andr\'es N. Salcedo, David H. Weinberg, Lehman H., Garrison, Douglas Ferrer, Jeremy Tinker, Daniel Eisenstein, Marc Metchnik,, Philip Pinto

TL;DR
This paper demonstrates that extending galaxy clustering and galaxy-galaxy lensing analyses into the deeply nonlinear regime significantly improves constraints on matter clustering parameters, utilizing simulations and emulators to forecast results.
Contribution
It introduces a Taylor-expansion emulator for nonlinear galaxy clustering and lensing, enabling improved parameter constraints by including small-scale data.
Findings
Nonlinear scales improve constraints on $\sigma_8\, ext{and}\,\Omega_m$ by a factor of two.
Including scales down to 0.5 h^{-1} Mpc enhances the constraining power.
Forecasts show 1% level matter clustering measurements are achievable with current data.
Abstract
The combination of galaxy-galaxy lensing (GGL) with galaxy clustering is one of the most promising routes to determining the amplitude of matter clustering at low redshifts. We show that extending clustering+GGL analyses from the linear regime down to Mpc scales increases their constraining power considerably, even after marginalizing over a flexible model of non-linear galaxy bias. Using a grid of cosmological N-body simulations, we construct a Taylor-expansion emulator that predicts the galaxy autocorrelation and galaxy-matter cross-correlation as a function of , , and halo occupation distribution (HOD) parameters, which are allowed to vary with large scale environment to represent possible effects of galaxy assembly bias. We present forecasts for a fiducial case that corresponds to BOSS LOWZ galaxy…
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